{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ae46c498",
   "metadata": {},
   "source": [
    "# Finding discords of any length in a time series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c541d34a",
   "metadata": {},
   "source": [
    "This tutorial explains the MERLIN algorithm, proposed in [MERLIN](https://www.cs.ucr.edu/~eamonn/MERLIN_Long_version_for_website.pdf). The support webpage can be found here: [MERLIN: SUPPORT](https://sites.google.com/view/merlin-find-anomalies).\n",
    "\n",
    "The algorithm discovers the discords of arbitrary length in time series. It is worthwhile to note that the term arbitrary means the user can define a range for the length of discord (i.e. minimum length, `min_m`, and maximum length, `max_m`), and the algorithm finds discords of different lengths $m \\in [min\\_m, max\\_m]$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3222ef3",
   "metadata": {},
   "source": [
    "## What is a discord?\n",
    "A subsequence of length `m` in a time series `T` is a discord **iff** it has the largest distance (hereafter being referred to as `discord_dist`) to its [first] nearest neighbor (NN). The neighbors of a subsequence of length `m`, starting at index `i`, is any other subsequences that start at an index `j`, such that `j!=i`. However, if `i` and `j` are very close to each other, the comparison between their corresponding subsequences is considered to be trivial. To this end, an exclusion zone (`excl_zone`) is taken into account to ignore the neighbors (i.e. subsequences) whose starting index is in numpy indexing `[i-excl_zone : i+excl_zone+1]`. \n",
    "\n",
    "**NOTE:** <br>\n",
    "It is important to note that for the i-th subsequence (i.e. `S = T[i:i+m]`), some of its neighbors are located on the left side of `S` (i.e. the ones with starting index less than/equal to `max(0, i-excl_zone-1)`) and some of its neighbors are located on the right side of `S` (i.e. the ones with starting index greater than/equal to `min(len(T), i+excl_zone+1)`). To find the NN of a subsequence `S`, the distance between `S` and all of its [left and right] neighbors must be obtained."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83e21524",
   "metadata": {},
   "source": [
    "## Matrix Profile approach"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ee019bb",
   "metadata": {},
   "source": [
    "How can we discover a discord of length `m` using matrix profile(`P`)? The solution is straightforward. `P` shows the distance of each subsequence to its NN. Therefore, the one that has the greatest distance to its NN is considered as the discord.\n",
    "\n",
    "* **Advantage** <br>\n",
    "Once we have the `P`, finding the discord is easy. Also, one can obtain the `top-k` discords very quickly.\n",
    "\n",
    "* **Disadvantage** <br>\n",
    "`P` needs to be calculated for each new length `m` in `[min_m, max_m]`, and, consequently, all pair-wise distances between subsequences must be calculated again. Because, ALL pairwise distances are required for obtaining `P`. \n",
    "\n",
    "As will be shown later, `MERLIN` can skip some  of the pair-wise distance calculations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "199cc02c",
   "metadata": {},
   "source": [
    "## MERLIN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a84a2c26",
   "metadata": {},
   "source": [
    "There are two main ideas at the core of the `MERLIN` algorithm. In below, we briefly explain each concept. Then, we will show its implementation and discuss its performance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4397ad73",
   "metadata": {},
   "source": [
    "### Idea (1): Elimination Approach\n",
    "The idea can be explained as follows: Suppose we are told that the discord distance (`discord_dist`), that is, the distance between the discord and its nearest neighbor, for subsequences of length `m` is at least `r` (Please note that the \"how\" of setting `r` will be explained later in Idea(2)). That means the distance between the discord and each one of its neighbors is at least `r`. We start scanning the subsequences. If, for a subsequence `S`, we realize that it has a neighbor to which its distance is smaller than `r`, we ignore `S`.\n",
    "\n",
    "The main idea is to eliminate all subsequences for which there exist at least one neighbor with pair-wise distance less than `r`. Therefore, the remaining subsequences (i.e. candidates) are the ones that have a distance great than/equal to `r`. Now that we narrowed down the candidates, we can search among them and find the NN of each candidate and discover the discord. \n",
    "\n",
    "**Note:** We can think of `r` as an estimate of discord_dist. Ideally, we would like to set this value to the discord distance or just a little less than the discord distance. However, the distance is unknown at this point, so the user can provide an estimate `r`. The provided value `r` might be an underestimation of true discord distance or an overestimation (We consider the case `r <= discord_dist` as underestimation here) If it is an underestimation, it means discord distance is at least `r`. Therefore, we will end up with some (at least 1) candidates that have distances `>=r` with all of their neighbors. However, if we overestimate `r`, we find no candidates (and therefore, we cannot find discord). To this end, we need to reduce the threshold `r` and try again. However, if we reduce `r` drastically, we may end up with a very bad underestimation of discord distance, which can lead to lots of candidates. But, what we are after is a small number of candidates so that we can search among them for discords in a short amount of time.\n",
    "\n",
    "---\n",
    "\n",
    "The smaller number of candidates, the better. This is where choosing a good value for `r` becomes important. For instance, let us consider two very extreme scenarios:\n",
    "\n",
    "**Scenario I:** Choosing a VERY SMALL value for `r`. In this case, we can end up with almost all subsequnces as the candidates.\n",
    "\n",
    "**Scenario II:** Choosing a VERY LARGE value for `r`. In this case, we can end up with no candidates at all. \n",
    "\n",
    "\n",
    "### Idea (2): Choosing `r`\n",
    "The value of `r` can be set by the user. However, this is very rare as user usually do not know the proper value for `r`. In MERLIN algorithm, the parameter `r` is initially set to the largest possible value (more on this below). And, then the algorithm tries to gradually reduce it in an iterative manner till it finds at least one candidate."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3d8a9aa",
   "metadata": {},
   "source": [
    "## z-normalized and non-normalized Euclidean distance <br>\n",
    "Please note that the [MERLIN](https://www.cs.ucr.edu/~eamonn/MERLIN_Long_version_for_website.pdf) paper used z-normalized euclidean distance to calculate the distance between any two subsequence. In this work, our focus is on both normalized- and non-normalized- Euclidean distances. In the following, we will show how we can use dot product to calculate these two distances. Using dot product will help us to use the power of vectorization in the (more efficient) implementation of the  algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ede8adaf",
   "metadata": {},
   "source": [
    "**z-normalizd Eucludiean distance ($ED_{z-norm}$):** <br>\n",
    "$ED_{z-norm}$ and the Pearson correlation ($PC$), between any two subsequences with length $m$, are related as follows [(berthold2016)](https://arxiv.org/pdf/1601.02213.pdf):\n",
    "\n",
    "$ED_{z-norm} = \\sqrt{2 \\times m \\times (1-PC)}$ \n",
    "\n",
    "On the other hand, the $PC$ can be calculated as $PC = \\frac{E[(X-\\mu_{X})(Y-\\mu_{Y})]}{\\sigma_{X}\\sigma_{Y}}$, which can be re-written as follows:\n",
    "\n",
    "$PC = \\frac{{\\frac{1}{m}}{\\sum\\limits_{t=1}^{m}} {(X_{t} - \\mu_{X})((Y_{t} - \\mu_{Y}))}}{\\sigma_{X}\\sigma_{Y}}$.\n",
    "\n",
    "Please note that a z-normalized subsequence has a mean ($\\mu$) of 0 and standard deviation ($\\sigma$) of 1. Therefore, the pearson correlation between two z-normalized subsequences becomes:\n",
    "\n",
    "$PC = {\\frac{1}{m}} <X_{z}, Y_{z}>$, where $<X_{z}, Y_{z}>$ is the dot product between two vectors $X_{z}$ and $Y_{z}$, where $X_{z}$ ($Y_{z}$) is a vector consists of the values of the subequene obtained by z-normalizing the subsequence $X$ ($Y$).\n",
    "\n",
    "\n",
    "So, the first formula becomes: <br>\n",
    "$ED_{z-norm} = \\sqrt{2 \\times m \\times (1 - {\\frac{1}{m}} <X_{z}, Y_{z}>)}$ \n",
    "\n",
    "\n",
    "\n",
    "Therefore, for a given $ED_{z-norm}$, one can calculate its corresponding dot product value as below: <br>\n",
    "$<X_{z}, Y_{z}> = m \\times (1 - \\frac{1}{2m}{ED_{z-norm}^{2}})$ <br>\n",
    "Thus, instead of comparing two z-normlized subsequences by calculating their (z-normlized) Euclidean distance, one can calculate the dot product of the two z-normalized subsequences. Please note that a higher z-normalized Euclidean distance means lower dot product value. So, instead of using `r` as the minimum distance a discord's candidate should have with all of its neighbors, one can use $m \\times (1 - \\frac{1}{2m}{r^{2}})$ as the maximum dot product value a discord's candidate should have with its neighbors.\n",
    "\n",
    "**Initial value for $ED_{z-norm}$**: <br>\n",
    "As discussed in [MERLIN](https://www.cs.ucr.edu/~eamonn/MERLIN_Long_version_for_website.pdf), the initial value for `r` is set to its maximum possible value. This can be achieved by choosing the lowest value for $PC$ (i.e. -1). In that case, it can be observed that the initial value for `r` is $2\\sqrt{m}$.\n",
    "\n",
    "---\n",
    "**non-normalizd Eucludiean distance ($ED$):** <br>\n",
    "$ED$ can still be calculated by the help of some dot products: <br>\n",
    "\n",
    "$ED^{2} = {||X-Y||}^{2}  =  <X-Y , X-Y>  =  <X.X> + <Y.Y>  -  2<X,Y>$ <br>\n",
    "Using the right hand side of the equation above may not make sense when we are iterating the subsequences in a for-loop one by one. However, in the more efficient version of MERLIN, where candidates are compared to more than one subsequencec in each iteration, this relationship can be helpful. We can calculate and store the two-norm of all subsequences in a 1-dim array. Then, we can use np.matmul() to calculate the last term (i.e. $<X,Y>$). We can get into further details later in the implementation.\n",
    "\n",
    "**Initial value for $ED$**: <br>\n",
    "Similar to the z-normalized case, we would like to set the value of the `r` to the maximum possible value. Therefore:\n",
    "\n",
    "$min\\_dist^{2} = max ({||X-Y||}^{2}) = max \\sum\\limits_{t=1}^{m}(X_{t} - Y_{t})^{2} =  \\sum\\limits_{t=1}^{m}max{(X_{t} - Y_{t})^{2}} = \\sum\\limits_{t=1}^{m}{[max (|X_{t} - Y_{t}|)]}^{2} =  \\sum\\limits_{t=1}^{m}{(T_{max} - T_{min})^2} = {m} \\times{(T_{max} - T_{min})^2}$\n",
    "\n",
    "where, $T_{max}$ ($T_{min}$) is the maximum (minimum) value of the main time series $T$.\n",
    "\n",
    "And, this gives the maximum possible value for `r` as follows: <br>\n",
    "$ min\\_dist = (|T_{max} - T_{min}|) \\sqrt{m}$\n",
    "\n",
    "**NOTE:** Please note that, in practice, `T` might have infinite/nan values. In that case, `r` becomes infinite (and thus useless). To avoid this scenario, we should do: $T_{max} = np.max(T[np.isfinite(T)])$, and $T_{min} = np.min(T[np.isfinite(T)])$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eda49031",
   "metadata": {},
   "source": [
    "**Alternative Approach for initializing `r`**<br>\n",
    "The algorithm MERLIN uses the highest possible distance between two subsequences as the initial value for `r`. However, there is an alternative approach. In STUMPY, `_prescrump` gives an approximate matrix profile which is an overestimation of the true matrix profile. Therefore, instead of setting `r` to the highest possible value (e.g, $2*\\sqrt{m}$ in normalize case), we can simply set it to the highest value in approximate matrix profile."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bd5968f",
   "metadata": {},
   "source": [
    "## Implement MERLIN\n",
    "It is worthwhile to note that a faster version of MERLIN is provided on the MERLIN's support webpage [MERLIN: SUPPORT](https://sites.google.com/view/merlin-find-anomalies). However, for now, we implement the version proposed in the original paper as it can be implemented in a cleaner/more understandable way."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdb169bf",
   "metadata": {},
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a40d0bd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import stumpy\n",
    "from stumpy import core, config\n",
    "from stumpy.scrump import _prescrump\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import time\n",
    "\n",
    "plt.style.use('https://raw.githubusercontent.com/TDAmeritrade/stumpy/main/docs/stumpy.mplstyle')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e812ecb0",
   "metadata": {},
   "source": [
    "### Import (toy) data\n",
    "data set is available at: \n",
    "https://drive.google.com/file/d/1cDkZVKYse_E0_fGZqTRQZrrMBRFrR2Mv/view\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7d83b0b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.io import loadmat\n",
    "\n",
    "data = loadmat(\"MERLIN_datasets\\\\NoisySine.mat\") \n",
    "ts = data['T'].reshape(-1,)\n",
    "\n",
    "#visualize data\n",
    "plt.plot(ts)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50733892",
   "metadata": {},
   "source": [
    "As we can see there is an anomaly located in about the middle of the time series data. We will implement MERLIN to discover the discord."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b2a6412",
   "metadata": {},
   "source": [
    "### MERLIN- Part (I): DRAG --> Find Candidates  \\[for discord of length m\\]\n",
    "As explained before, the idea is to select a number of candidates by eliminating undesirable subsequences. A candidate is a subsequence whose distance to all of its neighbors are at least `r`. Then, we can search among these candidates and find the discord. \n",
    "\n",
    "This part can be done in two phases as follows:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "877bb3e0",
   "metadata": {},
   "source": [
    "**MERLIN-Part(I): DRAG - Phase (I) --> Find Candidates** <br>\n",
    "In this phase, we compare each subsequence (i.e. potential candidate) with ALL of its RIGHT, and then LEFT neighbors (or vice versa)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3332f6af",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _get_chunks_ranges(a, shift=None):\n",
    "    \"\"\"\n",
    "    This function takes an array that contains only integer numbers in ascending order, and return the\n",
    "    `(inclusive) start index` and `(exclusive) stop index + shift` for each continuous segment of array.\n",
    "    \n",
    "    Parameters\n",
    "    --------\n",
    "    a : numpy.ndarray\n",
    "        1-dim array that contains integer numbers in ascending order.\n",
    "    \n",
    "    shift : int, default None\n",
    "        an integer number by which the stop index of each segement should be shifted. If None, no shift will be applied.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    out : numpy.ndarray\n",
    "        a 2-dim numpy array. The first column is the (inclusive) start index of each segment. The second column is the\n",
    "        (exclusive) stop index shifted by `shift` units.\n",
    "    \"\"\"    \n",
    "    repeats = np.full(len(a), 2)\n",
    "    diff_is_one = np.diff(a) == 1\n",
    "    repeats[1:] -= diff_is_one\n",
    "    repeats[:-1] -= diff_is_one\n",
    "    out = np.repeat(a, repeats).reshape(-1, 2)\n",
    "    out[:, 1] += 1\n",
    "    \n",
    "    if shift is not None:\n",
    "        out[:, 1] += shift\n",
    "\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8e14e424",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _find_candidates(T, m, M_T, Σ_T, r, init_cands=None, right=True, finite=False):\n",
    "    \"\"\"\n",
    "    For a time series T, this function finds a set of candidates whose distance to all of their right (left) neighbors \n",
    "    is at least `r` when parameter `right` is TRUE (FALSE). If there is no such candidate, all elements of is_cands\n",
    "    becomes False.\n",
    "    \n",
    "    Parameters\n",
    "    ---------\n",
    "    T : numpy.ndarray\n",
    "        The time series or sequence from which the candidates are being selected.\n",
    "    \n",
    "    m : int\n",
    "        Window size\n",
    "    \n",
    "    M_T : ndarray\n",
    "        Sliding mean of `T`\n",
    "    \n",
    "    Σ_T : ndarray\n",
    "        Sliding standard deviation of `T`\n",
    "    \n",
    "    r : float \n",
    "        An estimate of discord_dist. The selected candidates retuned by this function have distances of at least `r` \n",
    "        to all of their right(left) neighbors when input `right` is set to True(False).\n",
    "        \n",
    "        Choosing different values for `r`can affect the performance of the algorithm \n",
    "        (see Fig. 5 of the paper). For instance, choosing a very large value for `r` may result in no candidates \n",
    "        while choosing a very small value may result in a lot of candidates.  \n",
    "        (note: `r` is passed to this private function when it is called inside the top-level function `_discords`).\n",
    "    \n",
    "    init_cands : numpy.ndarray, default None\n",
    "        is a 1-dim boolean array, with shape=(k,) where `k` is the total number of subsquences in the time series. \n",
    "        `init_cands[i]` is True if the subsequence with start index `i` is considered as one of the \n",
    "        prospective candidates.\n",
    "        \n",
    "    right : bool, default True\n",
    "        If True (False), candidates returned by the function are guaranteed to have at least the distance of `r` \n",
    "        to all of their 'right`('left') neighbors.\n",
    "    \n",
    "    finite : bool, default False\n",
    "        If True, subsequence with infinite values will not be considered as candidates.   \n",
    "    \n",
    "    Returns\n",
    "    --------\n",
    "    is_cands : numpy.ndarray\n",
    "        is a 1-dim boolean array, with shape=(k,) where `k` is the total number of subsquences in the time series. \n",
    "        `is_cands[i]` is True if the subsequence with start index `i` has minimum distance of `r` to all of its \n",
    "        right (left) neighbors when right is True (False).\n",
    "    \n",
    "    NOTE\n",
    "    -------    \n",
    "    Unlike the MERLIN paper where the exclusion zone is m, the default exclusion zone considered here\n",
    "    is the STUMPY default config m/4. This can be changed by setting config.STUMPY_EXCL_ZONE_DENOM.\n",
    "    \"\"\"    \n",
    "    excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))\n",
    "    \n",
    "    k = T.shape[0] - m + 1 \n",
    "    \n",
    "    is_cands = np.ones(k, dtype=bool)\n",
    "    if init_cands is not None:\n",
    "        is_cands[:] = init_cands\n",
    "    \n",
    "    T_subseq_isfinite = np.isfinite(M_T)\n",
    "    if not finite:\n",
    "        T_subseq_isfinite[:] = True\n",
    "    is_cands[~T_subseq_isfinite] = False\n",
    "    \n",
    "    for i in np.flatnonzero(T_subseq_isfinite):\n",
    "        if np.all(is_cands == False):\n",
    "            break\n",
    "            \n",
    "        cands_idx = np.flatnonzero(is_cands)\n",
    "        \n",
    "        if right: \n",
    "            non_trivial_cands_idx = cands_idx[cands_idx < max(0, i - excl_zone)]\n",
    "        else:\n",
    "            non_trivial_cands_idx = cands_idx[cands_idx > i + excl_zone]\n",
    "        \n",
    "        if len(non_trivial_cands_idx) > 0:        \n",
    "            cand_idx_chunks = _get_chunks_ranges(non_trivial_cands_idx, shift=m-1) \n",
    "            #shift=m-1: convert from subsequence space to time series space\n",
    "            \n",
    "            for start, stop in cand_idx_chunks:\n",
    "                QT = core._sliding_dot_product(T[i:i+m], T[start:stop]) \n",
    "                D = core._mass(T[i:i+m], T[start:stop], QT, M_T[i], Σ_T[i], M_T[start:stop-m+1], Σ_T[start:stop-m+1])\n",
    "\n",
    "                mask = np.flatnonzero(D < r)   \n",
    "                is_cands[start:stop-m+1][mask] = False\n",
    "\n",
    "                if len(mask):\n",
    "                    is_cands[i] = False\n",
    "        \n",
    "    return is_cands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f543e846",
   "metadata": {},
   "outputs": [],
   "source": [
    "#input (as provided by the  authors of paper)\n",
    "m = 512 \n",
    "r = 10.27 #r is not required for MERLIN. This is just to show the code works in this private function.\n",
    "\n",
    "T, M_T, Σ_T = core.preprocess(ts, m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "67349676",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 584,  585,  586,  587,  588,  589,  590,  591,  592,  593,  594,\n",
       "        595,  596,  597,  598,  599,  600,  601,  602,  603,  604,  613,\n",
       "        614,  615,  616,  617,  618,  619,  620,  621,  622,  623,  624,\n",
       "        625,  626,  627,  628,  629,  630,  631,  632,  633,  634,  646,\n",
       "        647,  648,  649,  650,  651,  652,  653,  654,  655,  656,  657,\n",
       "        658,  659,  660,  661,  662,  663,  664,  665,  677,  678,  679,\n",
       "        680,  681,  682,  683,  684,  685,  686,  687,  688,  689,  690,\n",
       "        691,  692,  693,  694,  695,  696,  710,  711,  712,  713,  714,\n",
       "        715,  716,  717,  718,  719,  720,  749,  750,  751,  780,  781,\n",
       "        906,  907,  908, 1361, 1362, 1363, 1364, 1367, 1368, 1369, 1370,\n",
       "       1371, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388,\n",
       "       1393, 1394, 1395, 1396, 1400, 1401, 1402, 1403, 1404, 1405, 1406,\n",
       "       1407, 1408, 1409, 1410, 1411, 1415, 1416, 1417, 1418, 1419, 1421,\n",
       "       1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432,\n",
       "       1433, 1434, 1435, 1436, 1437, 1439, 1440, 1441, 1442, 1443, 1444,\n",
       "       1445, 1446, 1447, 1448, 1449, 1450, 1451, 1470, 1471, 1472, 1473,\n",
       "       1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1488,\n",
       "       1489], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_cands = _find_candidates(T, m, M_T, Σ_T, r, init_cands=None, right=True)\n",
    "cand_index = np.flatnonzero(is_cands)\n",
    "cand_index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1960307",
   "metadata": {},
   "source": [
    "The number of so-far-selected candidates (188) are about one-tenth of total number of subsequences (1490). Also, some of them might be False Positives (that will be handled when scanning neighbors of the other direction.). \n",
    "\n",
    "Let's see if the anomaly part of the time series is covered by the selected candidates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5272f085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(T, c='b')\n",
    "for idx in cand_index:\n",
    "    plt.plot(np.arange(idx,idx+m), T[idx:idx+m], c='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "197c12fc",
   "metadata": {},
   "source": [
    "As illustrated, the selected candidates cover the anomaly. Next, we use the same function to compare subsequences with their left neighbors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e0c7b46c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([592, 593, 594, 621, 622, 623, 624, 625, 626, 627, 652, 653, 654,\n",
       "       655, 656, 657, 658, 659, 683, 684, 685, 686, 687, 688, 689, 690,\n",
       "       691, 715, 716, 717, 718, 719, 720, 749, 750, 751, 780, 781, 906,\n",
       "       907, 908], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_cands = _find_candidates(T, m, M_T, Σ_T, r, init_cands=is_cands, right=False)\n",
    "cands = np.flatnonzero(is_cands)\n",
    "cands"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d888c04",
   "metadata": {},
   "source": [
    "The number of final candidates (41) is about 3% of total number of subsequences (1490). In other words, out of the initial number of candidates (i.e. 1490), we just need to search among the 41 final candidates. \n",
    "\n",
    "**Check if the dicord is covered by final candidates** <br>\n",
    "Let us see if the anomaly part of the time series is covered by these final candidates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1aa1d9cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(T, c='b')\n",
    "for idx in cands:\n",
    "    plt.plot(np.arange(idx,idx+m), ts[idx:idx+m], c='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6be2e116",
   "metadata": {},
   "source": [
    "As illustrated in the figure above, the candidate covers the anomaly of the time series data. Please note that it is possible that some of the candidates may not cover the anomaly. For example, recall the scenario where `r` is very small. In that case, most subsequences are going to be returned as candidates. So, it is NOT reasonable to expect all candidates cover the anomaly. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81e912c0",
   "metadata": {},
   "source": [
    "**MERLIN-Part(I): DRAG - Phase (II) --> Find Discord**<br>\n",
    "After pruning the false positive candidates, we can find the NN of each of the remaining candidates. The candidate that has the greatest distance to its NN is the top-discord of that set of candidates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e2c09b9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _get_approx_P(T, m, M_T, Σ_T, s):\n",
    "    \"\"\"\n",
    "    This function returns the (approximate) matrix profile. \n",
    "    \n",
    "    Parameters\n",
    "    ---------\n",
    "    T : numpy.ndarray\n",
    "        The time series or sequence for which the approximate matrix profile is calculated.\n",
    "    \n",
    "    m : int\n",
    "        Window size\n",
    "    \n",
    "    M_T : ndarray\n",
    "        Sliding mean of `T`\n",
    "    \n",
    "    Σ_T : ndarray\n",
    "        Sliding standard deviation of `T`\n",
    "    \n",
    "    s : int\n",
    "        The sampling interval\n",
    "    \n",
    "    Returns\n",
    "    ---------\n",
    "    P : numpy.ndarray\n",
    "        Matrix profile\n",
    "    \"\"\"\n",
    "    excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))\n",
    "    \n",
    "    k = T.shape[0] - m + 1\n",
    "    \n",
    "    indices = np.random.permutation(range(0, k, s)).astype(np.int64)\n",
    "    P, _ = _prescrump(\n",
    "        T,\n",
    "        T,\n",
    "        m,\n",
    "        M_T,\n",
    "        Σ_T,\n",
    "        M_T,\n",
    "        Σ_T,\n",
    "        indices,\n",
    "        s,\n",
    "        excl_zone,\n",
    "    )\n",
    "    \n",
    "    return P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f504fc53",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _refine_candidates(T, m, M_T, Σ_T, is_cands):\n",
    "    \"\"\"\n",
    "    For a time series `T`, this function searches the candidates (i.e. subsequences indicated by `is_cands`) and \n",
    "    return candidates discords in descending order according to their distance to their nearest neighbor.\n",
    "    After finding the top-discord among candidates, the discord subsequence and its trivial neighbors will be excluded \n",
    "    from candidates before finding the next top-discord.\n",
    " \n",
    "    Parameters\n",
    "    ---------\n",
    "    T : numpy.ndarray\n",
    "        The time series or sequence from which the top discord (out of selected candidates) is discovered. \n",
    "    \n",
    "    m : int\n",
    "        Window size\n",
    "    \n",
    "    M_T : numpy.ndarray\n",
    "        Sliding mean of `T`\n",
    "    \n",
    "    Σ_T : numpy.ndarray\n",
    "        Sliding standard deviation of `T`\n",
    "    \n",
    "    is_cands : numpy.ndarray\n",
    "        is a 1-dim boolean array, with shape=(k,) where `k` is the total number of subsquences in the time series. \n",
    "        when `is_cands[i]` is True, a subsequence with start index `i` is a discord candidate.\n",
    "     \n",
    "    Returns\n",
    "    ---------\n",
    "    out : numpy.ndarray\n",
    "        is a 2-dim array with three columns. The first column is indices of discords, sorted according to their \n",
    "        corresponding distances to their nearest neighbor, provided in the second column. \n",
    "        The third column is the indices of the discords' nearest neighbor. \n",
    "    \"\"\"    \n",
    "    excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM)) \n",
    "    k = T.shape[0] - m + 1\n",
    "    \n",
    "    P = np.full(k, np.NINF, dtype=np.float64) # matrix profile\n",
    "    I = np.full(k, -1, dtype=np.int64) # index of Nearest Neighbor \n",
    "    \n",
    "    for idx in np.flatnonzero(is_cands): \n",
    "        Q = T[idx:idx+m]\n",
    "        QT = core._sliding_dot_product(Q, T)\n",
    "        D = core._mass(Q, T, QT, M_T[idx], Σ_T[idx], M_T, Σ_T)\n",
    "        core.apply_exclusion_zone(D, idx, excl_zone, val=np.inf)\n",
    "        \n",
    "        nn_idx = np.argmin(D)  \n",
    "        if D[nn_idx] == np.inf:\n",
    "            nn_idx = -1\n",
    "        P[idx] = D[nn_idx]\n",
    "        I[idx] = nn_idx\n",
    "    \n",
    "    discords_idx = []\n",
    "    discords_dist = []\n",
    "    discords_nn_idx = [] \n",
    "    while np.any(P>=0):\n",
    "        idx = np.argmax(P)\n",
    "        discords_idx.append(idx)\n",
    "        discords_dist.append(P[idx])\n",
    "        discords_nn_idx.append(I[idx])  \n",
    "        core.apply_exclusion_zone(P, idx, excl_zone, np.NINF)\n",
    "     \n",
    "    return discords_idx, discords_dist, discords_nn_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dbeda75a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the index of discord is:  [718, 906]\n",
      "distance of discord to its NN is:  [10.301397123538967, 10.271812911147354]\n",
      "the index of NearestNeighbor of the discord is:  [278, 1283]\n"
     ]
    }
   ],
   "source": [
    "s = int(0.001 * T.shape[0])\n",
    "approx_P = _get_approx_P(T, m, M_T, Σ_T, s)\n",
    "discords_idx, discords_dist, discords_nn_idx = _refine_candidates(T, m, M_T, Σ_T, is_cands)\n",
    "\n",
    "print('the index of discord is: ', discords_idx)\n",
    "print('distance of discord to its NN is: ', discords_dist)\n",
    "print('the index of NearestNeighbor of the discord is: ', discords_nn_idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8711fa0f",
   "metadata": {},
   "source": [
    "**Now, let us use matrix profile (of stumpy package) to make sure the output of MERLIN-Part(I) is correct:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "77f5a86f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> using STUMPY matrix profile to find the discord <<<\n",
      "the index of discord is:  [718, 906]\n",
      "dist of discord to its nn is:  [10.301397123538928, 10.271812911147341]\n",
      "the index of nn of the discord:  [278, 1283]\n"
     ]
    }
   ],
   "source": [
    "excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))\n",
    "\n",
    "mp = stumpy.stump(T, m)\n",
    "P = mp[:,0].astype(np.float64) #p: matrix profile (dist of each subseq to its NN)\n",
    "\n",
    "mp_discords_idx = [] #np.argmax(P)\n",
    "mp_discords_dist = [] #P[mp_discord_idx]\n",
    "mp_discords_nn_idx = [] #mp[mp_discord_idx,1]\n",
    "\n",
    "for i in range(2): #2: number of discords discovered from candidates in _refine_candidates\n",
    "    if np.any(P>=0):\n",
    "        idx = np.argmax(P)\n",
    "        mp_discords_idx.append(idx)\n",
    "        mp_discords_dist.append(P[idx])\n",
    "        mp_discords_nn_idx.append(mp[idx,1])\n",
    "        core.apply_exclusion_zone(P, idx, excl_zone, np.NINF)\n",
    "    \n",
    "\n",
    "print('>>> using STUMPY matrix profile to find the discord <<<')\n",
    "print('the index of discord is: ', mp_discords_idx)\n",
    "print('dist of discord to its nn is: ', mp_discords_dist)\n",
    "print('the index of nn of the discord: ', mp_discords_nn_idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cbadc67",
   "metadata": {},
   "source": [
    "### Finding `top-k` Discords (of length m)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c9295f6",
   "metadata": {},
   "source": [
    "In some cases, one might be interested in finding more than one discord. The authors of [MERLIN](https://www.cs.ucr.edu/~eamonn/MERLIN_Long_version_for_website.pdf) referred readers to the paper [DiskawareDiscords](https://www.cs.ucr.edu/~eamonn/DiskawareDiscords.pdf) for `top-k` discords. However, the paper [DiskawareDiscords](https://www.cs.ucr.edu/~eamonn/DiskawareDiscords.pdf) does not take into account the `excl_zone` when it tries to find the `top-k` discords. In other words, it can return the subsequences at `idx` and `idx+1` as the `top-2` discords! But, this is not a correct approach as the two discovered discords are the trivial match of each other! As one can observe in the MATLAB implementation of MERLIN provided in [MERLIN: SUPPORT](https://sites.google.com/view/merlin-find-anomalies), the `k-th` discord index should not be in the `exclusion area` of the previous `k-1` discords."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "000cf392",
   "metadata": {},
   "source": [
    "**> To find `top-k` discords** <br>\n",
    "Let us assume we already discovered the the `top-k` discords. We can find the next discord (i.e the `k+1 -th` discord with length the same as the previously-discovered ones) as follows: <br>\n",
    "\n",
    "(1) reduce `r` (so we can get more candidates!) <br>\n",
    "(2) get candidates by function `_find_candidates()` while excluding the previously-discovered discords and their trivial matches. <br>\n",
    "(3) Find the discords by using `_refine_candidates` function. <br> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "795df761",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _discords(T, m, k=1, r=None, decay=None, s=None, include=None, finite=False):\n",
    "    \"\"\"\n",
    "    For a time series `T`, this function finds the top-k discords of length `m` with method MERLIN.\n",
    "\n",
    "    Parameters\n",
    "    ---------\n",
    "    T : numpy.ndarray\n",
    "        The time series or sequence from which to get the top-k discords\n",
    "\n",
    "    m : int\n",
    "        Window size\n",
    "\n",
    "    k : int\n",
    "        number of discords to be discovered.\n",
    "\n",
    "    r : float, default None\n",
    "        An initial value for r. An ideal value for r should be close to discord distance. \n",
    "        If you do not have a good guess about r, it is recommended leaving this parameter to None.\n",
    "        The smallest value allowed for r is config.STUMPY_MIN_DISCORD_DISTANCE, which is set to 1e-6 by default.\n",
    "        \n",
    "    decay: numpy.ndarray, default None\n",
    "        a 1-dim array of length k with values each between 0 and 1. \n",
    "        The decay[i] represents the rate of decrease of `r` for i-th discord. \n",
    "\n",
    "    s : int, default None\n",
    "        The sampling interval, that defaults to int(0.001 * len(T)).\n",
    "\n",
    "    include : ndarray, default None\n",
    "        is a 1-dim boolean array, whose length is the total number of subsquences in the time series.\n",
    "        `include[i]` is True if the subsequence with start index `i` is eligible to be considered as one of the\n",
    "        prospective candidates. Therefore, if `include[i]` is False, `is_cands[i]` will be False as well.\n",
    "        When include=None (default), all the elements of `include` are set to True.\n",
    "\n",
    "    finite : bool, default False\n",
    "        If True, subsequence with infinite values will be ignored.\n",
    "\n",
    "    Returns\n",
    "    --------\n",
    "    out : ndarray\n",
    "        has shape (k, 3). The i-th row cosists of information of i-th discord.\n",
    "        First column is the discord index. Second column is the distance of discard to its Nearest Neighbor.\n",
    "        And, third column is the index of discord's NearestNeighbor. The discords are sorted according to their \n",
    "        distances to their nearest neighbor. If  number of discovered discords is less than k, the remaining rows\n",
    "        are filled with [-1, np.NINF, -1].\n",
    "\n",
    "    NOTE:\n",
    "    (1) It is important to note that when `include[i]` is False, the subsequence `i` is still considered\n",
    "    as neighbors of other subsequences. This input is useful when a user wants to focus on detecting\n",
    "    anomaly of a portion of time series (while considering patterns in the whole time series `T` as neighbors).\n",
    "\n",
    "    (2) Please note that the rate of change for updating `r` is not science-backed.\n",
    "    In MERLIN paper, they used 0.99 in some cases, and 0.95 in other cases as the rate-of-change factor.\n",
    "    \n",
    "    (3) In contrast to original work MERLIN, we use approximate matrix profile, which can help us in narrowding down our \n",
    "    search space. \n",
    "    \"\"\"\n",
    "    T, M_T, Σ_T = core.preprocess(T, m)\n",
    "    excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))\n",
    "    n = T.shape[0]\n",
    "    l = n - m + 1\n",
    "    \n",
    "    if m < 3:\n",
    "        raise ValueError(f\"the length of subsequence, {m}, cannot be less than 3.\")\n",
    "\n",
    "    if decay is None:\n",
    "        decay = np.full(k, 0.99)\n",
    "    if np.any(decay <= 0.0) or np.any(decay >= 1.0):\n",
    "        raise ValueError(\"All decay values must be between 0.0 and 1.0\")\n",
    "\n",
    "    if s is None:\n",
    "        s = int(0.001 * n)\n",
    "    approx_P = _get_approx_P(T, m, M_T, Σ_T, s)\n",
    "\n",
    "    if include is None:\n",
    "        include = np.ones(l, dtype=bool)\n",
    "    if len(include) != l:\n",
    "        raise ValueError(\n",
    "            f\"The length of include ({len(include)}) does not match \" \n",
    "            f\"the total number of subsequences ({l})\"\n",
    "        )\n",
    "\n",
    "    if finite:\n",
    "        include[~np.isfinite(M_T)] = False\n",
    "\n",
    "    max_dist = 2.0 * np.sqrt(m) # better to do: max_dist = min(2.0 * np.sqrt(m), approx_P[include].max())\n",
    "    if r is None or r > max_dist:\n",
    "        r = max_dist\n",
    "    if r < 1e-6: # config.STUMPY_MIN_DISCORD_DISTANCE = 1e-6\n",
    "        raise ValueError(\n",
    "            f\" `r` ({r}) is less than `config.STUMPY_MIN_DISCORD_DISTANCE` ({config.STUMPY_MIN_DISCORD_DISTANCE}).\" \n",
    "            \"Try increasing `r` or decreasing `config.STUMPY_MIN_DISCORD_DISTANCE`.\"\n",
    "        ) \n",
    "      \n",
    "    discords_idx = np.full(k, -1, dtype=np.int64)\n",
    "    discords_dist = np.full(k, np.NINF, dtype=np.float64)\n",
    "    discords_nn_idx = np.full(k, -1, dtype=np.int64)\n",
    "    \n",
    "    i=0\n",
    "    r_updated = r\n",
    "    while np.any(include):\n",
    "        init_cands = include & (approx_P >= r_updated)\n",
    "        is_cands = _find_candidates(T, m, M_T, Σ_T, r_updated, init_cands=init_cands, right=True, finite=finite)\n",
    "        is_cands = _find_candidates(T, m, M_T, Σ_T, r_updated, init_cands=is_cands, right=False, finite=finite)\n",
    "        \n",
    "        if np.any(is_cands):\n",
    "            IDX, D, NN_IDX = _refine_candidates(T, m, M_T, Σ_T, is_cands)\n",
    "            for idx, dist, nn_idx in zip(IDX, D, NN_IDX):\n",
    "                discords_idx[i] = idx\n",
    "                discords_dist[i] = dist\n",
    "                discords_nn_idx[i] = nn_idx\n",
    "                core.apply_exclusion_zone(include, idx, excl_zone, val=False)\n",
    "                i += 1\n",
    "                if i==k:\n",
    "                    break\n",
    "            \n",
    "        if r_updated <= 1e-6 or i==k: # config.STUMPY_MIN_DISCORD_DISTANCE = 1e-6\n",
    "            break\n",
    "        r_updated = max(r_updated * decay[i], 1e-6) # config.STUMPY_MIN_DISCORD_DISTANCE = 1e-6\n",
    "    \n",
    "    \n",
    "    out = np.empty((k,3), dtype=object)\n",
    "    out[:,0] = discords_idx\n",
    "    out[:,1] = discords_dist\n",
    "    out[:,2] = discords_nn_idx\n",
    "    \n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a808d1b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running time:  1.599726676940918\n",
      "------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[718, 10.301397123538967, 278],\n",
       "       [906, 10.271812911147354, 1283],\n",
       "       [589, 10.245937568875549, 149],\n",
       "       [1035, 8.487378272524321, 1412],\n",
       "       [1182, 2.2518328767162363, 114],\n",
       "       [309, 2.2364873593725956, 1377],\n",
       "       [154, 2.230473678558997, 1348],\n",
       "       [1348, 2.230473678558997, 154],\n",
       "       [1479, 2.2014556687630877, 285],\n",
       "       [5, 2.1913006977345426, 382],\n",
       "       [438, 2.169638390989113, 61],\n",
       "       [-1, -inf, -1],\n",
       "       [-1, -inf, -1],\n",
       "       [-1, -inf, -1],\n",
       "       [-1, -inf, -1]], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tic = time.time()\n",
    "out = _discords(T, m, k=15)\n",
    "toc = time.time()\n",
    "print('running time: ', toc-tic)\n",
    "print('------------------------')\n",
    "out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74bb6408",
   "metadata": {},
   "source": [
    "**Let us use (STUMPY) matrix profile to find `top-k` discords**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fb58320b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stumpy_top_k_discords(T, m, k=1, finite=False):\n",
    "    \"\"\"\n",
    "    This funciton use stumpy package to find the top-k discords of length m with help of matrix profile.\n",
    "    \n",
    "    Parameters\n",
    "    ---------\n",
    "    T : numpy.ndarray\n",
    "        The time series or sequence from which to get the top-k discords\n",
    "    \n",
    "    m : int\n",
    "        Window size\n",
    "        \n",
    "    k : int\n",
    "        number of discords to be discovered.\n",
    "    \n",
    "    finite : bool, default False \n",
    "        If True, subsequence with infinite values will be ignored. \n",
    "    \n",
    "    Returns\n",
    "    --------\n",
    "    out : ndarray\n",
    "        has shape (k, 3). The i-th row cosists of information of i-th discord.\n",
    "        First column is the discord index. Second column is the distance of discard to its Nearest Neighbor.\n",
    "        And, third column is the index of discord's NearestNeighbor. The discords are sorted according to their \n",
    "        distances to their nearest neighbor. If  number of discovered discords is less than k, the remaining rows\n",
    "        are filled with [-1, np.NINF, -1].\n",
    "    \n",
    "    \"\"\"\n",
    "    excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))\n",
    "    \n",
    "    mp = stumpy.stump(T, m)\n",
    "    P = mp[:,0].astype(np.float64) #change the dtype to np.float64, so it can be used later in core.apply_exclusion_zone\n",
    "    \n",
    "    if finite:\n",
    "        P[~np.isfinite(P)] = np.NINF\n",
    "    \n",
    "    discords_idx = np.full(k, -1, dtype=np.int64)\n",
    "    discords_dist = np.full(k, np.NINF, dtype=np.float64)\n",
    "    discords_nn_idx = np.full(k, -1, dtype=np.int64)\n",
    "    \n",
    "    for i in range(k):\n",
    "        if np.all(P == np.NINF):\n",
    "            break\n",
    "        mp_discord_idx = np.argmax(P)\n",
    "        \n",
    "        discords_idx[i] = mp_discord_idx\n",
    "        discords_dist[i] = P[mp_discord_idx]\n",
    "        discords_nn_idx[i] = mp[mp_discord_idx,1]\n",
    "        \n",
    "        core.apply_exclusion_zone(P, discords_idx[i], excl_zone, val=np.NINF)\n",
    "    \n",
    "    out = np.empty((k, 3), dtype = object)\n",
    "    out[:, 0] = discords_idx\n",
    "    out[:, 1] = discords_dist\n",
    "    out[:, 2] = discords_nn_idx\n",
    "    \n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0fc1acea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[718, 10.301397123538928, 278],\n",
       "       [906, 10.271812911147341, 1283],\n",
       "       [589, 10.245937568875554, 149],\n",
       "       [1035, 8.487378272524301, 1412],\n",
       "       [1182, 2.25183287671611, 114],\n",
       "       [309, 2.2364873593723926, 1377],\n",
       "       [154, 2.2304736785589205, 1348],\n",
       "       [1348, 2.2304736785589205, 154],\n",
       "       [1479, 2.201455668762907, 285],\n",
       "       [5, 2.191300697734491, 382],\n",
       "       [438, 2.1696383909891916, 61],\n",
       "       [-1, -inf, -1],\n",
       "       [-1, -inf, -1],\n",
       "       [-1, -inf, -1],\n",
       "       [-1, -inf, -1]], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mp_out = stumpy_top_k_discords(T, m, k=15)\n",
    "mp_out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd5121a9",
   "metadata": {},
   "source": [
    "As observed, the outputs of `murlin` and `stumpy_top_k_discords` are the same in finding the `top-k` (`k=10`) discords (please see below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9e29425e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#compare the output of murlin and stumpy_top_k_discord\n",
    "np.testing.assert_almost_equal(mp_out[:-4], out[:-4]) #last four items are np.NINF"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5183bb6d",
   "metadata": {},
   "source": [
    "**Now, let us see the results when the time series has np.nan / np.inf values:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4352e217",
   "metadata": {},
   "outputs": [],
   "source": [
    "T[100:200] = np.inf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "53328520",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running time:  1.3154852390289307\n",
      "------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0, inf, -1],\n",
       "       [129, inf, -1],\n",
       "       [592, 10.309001435762784, 215],\n",
       "       [750, 10.276715760289077, 1127],\n",
       "       [906, 10.27181291114738, 1283],\n",
       "       [1035, 8.487378272524362, 1412],\n",
       "       [1223, 2.298055506977864, 406],\n",
       "       [1352, 2.259833665707362, 284],\n",
       "       [439, 2.2378115528550215, 1130],\n",
       "       [309, 2.236487359372723, 1377]], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tic = time.time()\n",
    "out = _discords(T, m, k=10)\n",
    "toc = time.time()\n",
    "print('running time: ', toc-tic)\n",
    "print('------------------------')\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5977542e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, inf, -1],\n",
       "       [129, inf, -1],\n",
       "       [592, 10.309001435762863, 215],\n",
       "       [750, 10.276715760289088, 1127],\n",
       "       [906, 10.271812911147409, 1283],\n",
       "       [1035, 8.487378272524396, 1412],\n",
       "       [1223, 2.298055506978037, 406],\n",
       "       [1352, 2.2598336657076135, 284],\n",
       "       [439, 2.2378115528551232, 1130],\n",
       "       [309, 2.2364873593730277, 1377]], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mp_out = stumpy_top_k_discords(T, m, k=10)\n",
    "mp_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "128bc196",
   "metadata": {},
   "outputs": [],
   "source": [
    "#compare the output of murlin and stumpy_top_k_discord for noisy data\n",
    "inf_mask = np.isinf(out[:,1].astype(np.float64))\n",
    "mp_inf_mask = np.isinf(mp_out[:,1].astype(np.float64))\n",
    "\n",
    "#check inf discods\n",
    "np.testing.assert_almost_equal(out[inf_mask][:,[0,2]], mp_out[mp_inf_mask][:,[0,2]]) \n",
    "\n",
    "#check finite discords\n",
    "np.testing.assert_almost_equal(out[~inf_mask], mp_out[~mp_inf_mask])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae94d567",
   "metadata": {},
   "source": [
    "### Try `_murlin` on a real-world data set, and compare it with STUMPY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d17340f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_taxi = pd.read_csv(\"MERLIN_datasets\\\\NAB_NYC_TAXI\\\\data\\\\realKnownCause\\\\nyc_taxi.csv\") \n",
    "df_taxi = df_taxi.set_index(['timestamp'])\n",
    "\n",
    "data =  df_taxi.loc['2014-10-01 00:00:00' : '2014-12-15 23:00:00']\n",
    "ts_taxi = np.reshape(data.to_numpy(dtype=np.float64), newshape=(-1,))\n",
    "\n",
    "T_taxi = [val for i,val in enumerate(ts_taxi) if i % 2 == 0]\n",
    "T_taxi = np.asarray(ts_taxi)\n",
    "\n",
    "plt.plot(T_taxi)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1881e721",
   "metadata": {},
   "outputs": [],
   "source": [
    "#set m and k\n",
    "m = 50; k = 10;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "94e665fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1494, 3.5268153024066717, 2502],\n",
       "       [1536, 3.4891959202175777, 192],\n",
       "       [2704, 3.4023316427088326, 2803],\n",
       "       [1518, 3.093376509535239, 846],\n",
       "       [2726, 2.770581012902818, 2871],\n",
       "       [2767, 2.642040969753919, 2818],\n",
       "       [2740, 2.6154277166261073, 578],\n",
       "       [2821, 2.2292277455863254, 2910],\n",
       "       [2781, 1.8949213350363567, 2831],\n",
       "       [2864, 1.8208725719146936, 1856]], dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#stumpy\n",
    "mp_out = stumpy_top_k_discords(T_taxi, m, k)\n",
    "mp_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d94a4164",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running time:  3.277238130569458\n",
      "--------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1494, 3.5268153024066797, 2502],\n",
       "       [1536, 3.4891959202175764, 192],\n",
       "       [2704, 3.402331642708777, 2803],\n",
       "       [1518, 3.09337650953523, 846],\n",
       "       [2726, 2.770581012902826, 2871],\n",
       "       [2767, 2.6420409697538583, 2818],\n",
       "       [2740, 2.6154277166261073, 578],\n",
       "       [2821, 2.229227745586343, 2910],\n",
       "       [2781, 1.8949213350364211, 2831],\n",
       "       [2864, 1.8208725719146996, 1856]], dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for now, we choose value of `r` based on the output of stumpy, just to see the outcomes of `_murlin`. \n",
    "#Later, we will see that `r` will be initialized and updated in the parent function `_discords`.\n",
    "\n",
    "tic = time.time()\n",
    "out = _discords(T_taxi, m, k)\n",
    "toc = time.time()\n",
    "\n",
    "print('running time: ', toc-tic)\n",
    "print('--------------------')\n",
    "\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ecbe9d53",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.testing.assert_almost_equal(mp_out, out)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d6b4bc2",
   "metadata": {},
   "source": [
    "**How about the performance of `_murlin` on a randomly-generated time series (from `np.random.uniform`)?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fa514899",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(seed=10)\n",
    "T = np.random.uniform(low=-100.0, high=100.0, size=5000)\n",
    "m = 50\n",
    "k = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d7f056f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running time:  0.04187321662902832\n",
      "--------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[2691, 7.737259840753032, 3303],\n",
       "       [4017, 7.6548012236676355, 3934],\n",
       "       [4426, 7.626027116981462, 2620],\n",
       "       [1508, 7.624255432471149, 4595],\n",
       "       [1417, 7.616814755546456, 2455]], dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#use STUMPY:\n",
    "tic = time.time()\n",
    "mp_out = stumpy_top_k_discords(T, m, k)\n",
    "toc = time.time()\n",
    "\n",
    "print('running time: ', toc-tic)\n",
    "print('--------------------')\n",
    "\n",
    "mp_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6096b57c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running time:  8.506269931793213\n",
      "--------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[2691, 7.7372598407530315, 3303],\n",
       "       [4017, 7.6548012236676355, 3934],\n",
       "       [4426, 7.626027116981461, 2620],\n",
       "       [1508, 7.62425543247115, 4595],\n",
       "       [1417, 7.616814755546457, 2455]], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tic = time.time()\n",
    "out = _discords(T, m, k)\n",
    "toc = time.time()\n",
    "\n",
    "print('running time: ', toc-tic)\n",
    "print('--------------------')\n",
    "\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "79bb13a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.testing.assert_almost_equal(mp_out, out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a7b0fe6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def discords(T, m, k=1, r=None, decay=None, s=None, include=None, finite=False):\n",
    "    \"\"\"\n",
    "    This funciton finds `top-k` discords for each length m in numpy indexing [min_m: max_m+1].\n",
    "    \n",
    "    Parameters\n",
    "    ---------\n",
    "    T : numpy.ndarray\n",
    "        The time series or sequence from which to get top-k discords\n",
    "    \n",
    "    m : int\n",
    "        Window size of discord.\n",
    "    \n",
    "    k: int, default 1\n",
    "        number of discords to discover (for each lenght of subsequence).\n",
    "    \n",
    "    r : float, default None\n",
    "        An initial value for r. An ideal value for r should be close to discord distance. \n",
    "        If you do not have a good guess about r, it is recommended leaving this input to None.\n",
    "        The smallest value allowed for r is config.STUMPY_MIN_DISCORD_DISTANCE, which is set to 1e-6 by default.\n",
    "\n",
    "    decay: numpy.ndarray\n",
    "        a 1-dim array of length k with values each between 0 and 1. \n",
    "        The decay[i] represents the rate of decrease of `r` for i-th discord. \n",
    "        \n",
    "    s : int, default None\n",
    "        The sampling interval, that defaults to int(0.001 * len(T)). \n",
    "        \n",
    "    include : ndarray\n",
    "        is a 1-dim boolean array, whose length is the total number of subsquences in the time series. \n",
    "        `include[i]` is True if the subsequence with start index `i` is eligible to be considered as one of the \n",
    "        prospective candidates. Therefore, if `include[i]` is False, `is_cands[i]` will be False as well. \n",
    "        When include=None (default), all the elements of `include` are set to True. \n",
    "    \n",
    "    finite: bool, default False\n",
    "        If True, subsequence with infinite values will be ignored.  \n",
    "    \n",
    "    Returns\n",
    "    --------\n",
    "    out: ndarray\n",
    "        has shape (k, 3). The i-th row cosists of information of i-th discord.\n",
    "        First column is the discord index. Second column is the distance of discard to its Nearest Neighbor.\n",
    "        And, third column is the index of discord's NearestNeighbor. The discords are sorted according to their \n",
    "        distances to their nearest neighbor. If number of discovered discords is less than k, the remaining rows\n",
    "        are filled with [-1, np.NINF, -1].\n",
    "    \n",
    "    \n",
    "    NOTE\n",
    "    --------\n",
    "    (1) Please note that higher values for decay may lead to a faster discovery \n",
    "        of candidates; however, it can result in a large set of candidates which can slow down the process. \n",
    "    \n",
    "    (2) To ignore trival matches, the original paper used a full window size m. However, this implementation uses the default \n",
    "        STUMPY setting of m/4.\n",
    "    \n",
    "    Ref:\n",
    "    DOI: 10.1109/ICDM50108.2020.00147\n",
    "    \"\"\"    \n",
    "    return _discords(T, m, k, r, decay, s, include, finite)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61fefeb6",
   "metadata": {},
   "source": [
    "## Bonus: Find discords for range of `m`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41ad4017",
   "metadata": {},
   "source": [
    "**Borrowed from MERLIN paper:** If you want to find discords for a range of `m`, you can then take advantage of input `r`. Example below shows how this works for getting top-2 discords for subsequence lengths-range `[500:506]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "70d5f9e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = loadmat(\"MERLIN_datasets\\\\NoisySine.mat\") #toy data\n",
    "T = data['T'].reshape(-1,)\n",
    "\n",
    "min_m = 500\n",
    "max_m = 505\n",
    "\n",
    "k=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6356a090",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[500 647 10.249998271106335 207]\n",
      " [500 864 10.2168841325517 1241]\n",
      " [501 647 10.23926271968037 207]\n",
      " [501 863 10.208112390984075 1240]\n",
      " [502 647 10.228567284411206 207]\n",
      " [502 774 10.196621139063918 1151]\n",
      " [503 717 10.220910104620353 277]\n",
      " [503 908 10.192931908753042 1285]\n",
      " [504 692 10.228598715473 252]\n",
      " [504 910 10.203003166459244 1287]\n",
      " [505 691 10.238772511211097 251]\n",
      " [505 911 10.213658314483652 1288]]\n"
     ]
    }
   ],
   "source": [
    "discords_list = []\n",
    "\n",
    "r = None\n",
    "for m in range(min_m, max_m+1): \n",
    "    top_k_discords = discords(T, m, k, r) \n",
    "    discords_list.append(top_k_discords)\n",
    "    r = top_k_discords[0,1]\n",
    "\n",
    "n_discords = [k]*(max_m - min_m + 1)\n",
    "out = np.empty((sum(n_discords), 4), dtype=object)\n",
    "out[:, 0] = np.repeat(np.arange(min_m, max_m+1), n_discords)\n",
    "out[:, 1:] = np.vstack(discords_list)\n",
    "\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3cf526a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[500 647 10.249998271106346 207]\n",
      " [500 864 10.216884132551765 1241]\n",
      " [501 647 10.23926271968037 207]\n",
      " [501 863 10.208112390984102 1240]\n",
      " [502 647 10.228567284411238 207]\n",
      " [502 774 10.196621139063884 1151]\n",
      " [503 717 10.220910104620325 277]\n",
      " [503 908 10.192931908753097 1285]\n",
      " [504 692 10.228598715472936 252]\n",
      " [504 910 10.20300316645917 1287]\n",
      " [505 691 10.238772511211103 251]\n",
      " [505 911 10.213658314483597 1288]]\n"
     ]
    }
   ],
   "source": [
    "#use stumpy to find top-k discords for subsequence length-range from `min_m` to `max_m`\n",
    "discords_list = []\n",
    "for m in range(min_m, max_m+1):\n",
    "    top_k_discords = stumpy_top_k_discords(T, m, k)\n",
    "    discords_list.append(top_k_discords)\n",
    "\n",
    "n_discords = [item.shape[0] for item in discords_list]\n",
    "mp_out = np.empty((sum(n_discords), 4), dtype=object)\n",
    "mp_out[:, 0] = np.repeat(np.arange(min_m, max_m+1), n_discords)\n",
    "mp_out[:, 1:] = np.vstack(discords_list)\n",
    "\n",
    "print(mp_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d31bf4f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#compare results\n",
    "np.testing.assert_almost_equal(mp_out, out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "558d11f4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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